인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2026.4
- 수록면
- 804 - 813 (10page)
- DOI
- 10.7840/kics.2026.51.4.804
이용수
초록· 키워드
This paper presents an advanced pipeline for detecting fast-moving objects in aerial surveillance videos captured by unmanned aerial vehicles (UAVs). The proposed system addresses the challenges posed by large frame intervals and motion blur by integrating two key preprocessing techniques: Real-Time Intermediate Flow Estimation(RIFE)-based temporal frame interpolation, which increases video frame rates from 30 frames per second(FPS) to 120 FPS, and Multi-Input Multi-Output U-Net Plus (MIMO U-NetPlus)-based deblurring, which enhances spatial clarity. These enhancements are combined with the YOLOv8n object detection model, improving detection accuracy without altering its core architecture. Experimental evaluations on the VisDrone2019-VID dataset demonstrate that the proposed method significantly outperforms the baseline YOLOv8n, achieving a mAP@0.5 of 0.968 and a mAP@0.5:0.95 of 0.881. The results confirm that the combination of temporal interpolation and deblurring effectively restores object continuity and sharpness, leading to substantial improvements in detection performance for drone-based monitoring applications. Although the proposed pipeline introduces considerable latency (1.23 FPS vs. 108.89 FPS for the baseline), this computational cost represents a justified trade-off for achieving the high accuracy and robustness essential for mission-critical surveillance tasks where baseline methods fail.
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목차
- ABSTRACT
- Ⅰ. Introduction
- Ⅱ. The RIME – Net Detection Pipeline
- Ⅲ. Experiment
- Ⅳ. Conclusions
- References
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